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Accurate Method for Solar Power Generation Estimation for Different PV (Photovoltaic Panels) Technologies

Author

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  • Aissa Meflah

    (Unité de Développement des Équipements Solaires, UDES, Centre de Développement des Energies Renouvelables, CDER, Bou Ismail, Tipaza 42415, Algeria)

  • Fathia Chekired

    (Unité de Développement des Équipements Solaires, UDES, Centre de Développement des Energies Renouvelables, CDER, Bou Ismail, Tipaza 42415, Algeria)

  • Nadia Drir

    (Faculty of Electrical Engineering, University of Science and Technology Houari Boumediene (USTHB), BP 32, El Alia, Bab. Ezzouar, Algiers 16111, Algeria)

  • Laurent Canale

    (CNRS, LAPLACE Laboratory, UMR 5213, 31062 Toulouse, France)

Abstract

In 2023, solar photovoltaic energy alone accounted for 75% of the global increase in renewable capacity. Moreover, this natural energy resource is the one that requires the least investment, which makes it accessible to developing countries. Increasing return on investment in these regions requires a particular evaluation of environmental parameters influencing PV systems performance. Higher temperatures decrease PV module efficiency and, as a result, their power output. Additionally, fluctuations in solar irradiance directly impact the energy generated by these systems. Consequently, it is essential for investors to improve accurate predictive models that assess the power generation capacity of photovoltaic systems under local environmental conditions. Therefore, accurate estimation of maximum power generation is then crucial for optimizing photovoltaic (PV) system performances and selecting suitable PV modules for specific climates. In this context, this study presents an experimental comparison of three maximum power prediction methods for four PV module types (amorphous silicon, monocrystalline silicon, micromorphous silicon, and polycrystalline silicon) under real outdoor conditions. Experimental data gathered over the course of a year are analyzed and processed for the four PV technologies. Three different methods taking into account environmental parameters are presented and analyzed. The first estimation method utilizes irradiance as the primary input parameter, while two additional methods incorporate ambient temperature and PV module temperature for enhanced accuracy. The performance of each method is evaluated using standard statistical metrics, including the root mean square error (RMSE) and coefficient of determination ( R 2 ). The results demonstrate the effectiveness of all three methods, with RMSE values ranging from 1.6 W to 3.8 W and R 2 values consistently above 0.95. The most appropriate method for estimating PV power output is determined by the specific type of photovoltaic module and the availability of meteorological parameters. This study provides valuable insights for selecting an appropriate maximum power prediction method and choosing the most suitable PV module for a given climate.

Suggested Citation

  • Aissa Meflah & Fathia Chekired & Nadia Drir & Laurent Canale, 2024. "Accurate Method for Solar Power Generation Estimation for Different PV (Photovoltaic Panels) Technologies," Resources, MDPI, vol. 13(12), pages 1-21, November.
    • Handle: RePEc:gam:jresou:v:13:y:2024:i:12:p:166-:d:1531074
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    References listed on IDEAS

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    1. Wassila Tercha & Sid Ahmed Tadjer & Fathia Chekired & Laurent Canale, 2024. "Machine Learning-Based Forecasting of Temperature and Solar Irradiance for Photovoltaic Systems," Energies, MDPI, vol. 17(5), pages 1-20, February.
    2. Mauro Tucci & Antonio Piazzi & Dimitri Thomopulos, 2024. "Machine Learning Models for Regional Photovoltaic Power Generation Forecasting with Limited Plant-Specific Data," Energies, MDPI, vol. 17(10), pages 1-21, May.
    3. Fahmi F Muhammad & Ali W Karim Sangawi & Suhairul Hashim & S K Ghoshal & Isam K Abdullah & Shilan S Hameed, 2019. "Simple and efficient estimation of photovoltaic cells and modules parameters using approximation and correction technique," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-19, May.
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